Results 51 to 60 of about 375,641 (168)

Spectral dimensionality reduction for HMMs [PDF]

, 2012
Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the usual slow methods like EM or Gibbs sampling.
Foster, Dean P., Rodu, Jordan, Ungar, Lyle H.   +2 more
core   +2 more sources

Quantum Discriminant Analysis for Dimensionality Reduction and Classification

, 2016
We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set.
Cong, Iris, Duan, Luming
core   +1 more source

Two-Stage Dimensionality Reduction for Social Media Engagement Classification

Applied Sciences
The high dimensionality of real-life datasets is one of the biggest challenges in the machine learning field. Due to the increased need for computational resources, the higher the dimension of the input data is, the more difficult the learning task will ...
Jose Luis Vieira Sobrinho, Flavio Henrique Teles Vieira, Alisson Assis Cardoso   +2 more
doaj   +1 more source

Spectral Dimensionality Reduction [PDF]

In this paper, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigen-decomposition (hence ...
Jean-François Paiement, Marie Ouimet, Nicolas Le Roux, Olivier Delalleau, Pascal Vincent, Yoshua Bengio   +5 more
core  

Dimensionality reduction with subgaussian matrices: a unified theory [PDF]

, 2014
We present a theory for Euclidean dimensionality reduction with subgaussian matrices which unifies several restricted isometry property and Johnson-Lindenstrauss type results obtained earlier for specific data sets.
Dirksen, Sjoerd
core  

Optimal Dimensionality Reduction using Conditional Variational AutoEncoder

Transactions on Cryptographic Hardware and Embedded Systems
The benefits of using Deep Learning techniques to enhance side-channel attacks performances have been demonstrated over recent years. Most of the work carried out since then focuses on discriminative models.
Sana Boussam, Mathieu Carbone, Benoît Gérard, Guénaël Renault, Gabriel Zaid   +4 more
doaj   +1 more source

Dimensionality reduction of quality of life indicators

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2012
Selecting indicators for assessing the quality of life at the regional level is not unambigous. Currently, there are no precisely defined indicators that would give comprehensive information about the quality of life on a local level.
Andrea Jindrová, Julie Poláčková
doaj   +1 more source

DIMENSIONALITY REDUCTION WITH IMAGE DATA [PDF]

A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a
Daniel Peña, Mónica Benito
core  

Cascade Support Vector Machines with Dimensionality Reduction

Applied Computational Intelligence and Soft Computing, 2015
Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing.
Oliver Kramer
doaj   +1 more source

Multiple Kernel Spectral Regression for Dimensionality Reduction

Journal of Applied Mathematics, 2013
Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples.
Bing Liu, Shixiong Xia, Yong Zhou
doaj   +1 more source